visions&of&the&wireless&future:&...
TRANSCRIPT
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Visions of the Wireless Future: Insights into Emerging Technologies
Dina Katabi Directory of Wireless@MIT
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CSAIL
LIDS/EECS
MTL EECS
SLOAN RLE EECS
Over 20 PIs and 50 Graduate Students
MECHE
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Wireless@MIT Partners
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Traditional Approach New Approach
Optimize within isolated layers
HW and Radios
Comms. and Coding
Network & Apps
Optimize across the layers
HW and Radios
Comms. and Coding
Network and Apps
Disruptive gains are unlikely Major opportunities!
Fundamental Architectural Change
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10x Higher Data Rates
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The FCC projects that the US will face a spectrum shortfall in 2013.
Looming Wireless Capacity Crunch
The iPhone 4 demo failed due to wireless congestion. Jobs’s reaction: “If you want to see the demos, shut off your laptops, turn off all these MiFi base stations, and put them on the floor, please.”
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MegaMIMO
Alleviates the capacity crunch by transmitting more bits per unit of spectrum
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Access Point 1
Access Point 2
Today’s Wireless Networks Ethernet
Access Point 3
User 2 User 3 User 1
Today, Access Points Can’t Transmit Together in the Same Channel
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Access Point 1
Access Point 2
Today’s Wireless Networks Ethernet
Access Point 3
User 2 User 3 User 1
Interference!
Today, Access Points Can’t Transmit Together in the Same Channel
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Access Point 1
Access Point 2
MegaMIMO Ethernet
Access Point 3
Access Points Can Transmit Simultaneously in the Same Channel
Interference: d2+d3≈0
Data: d1 survives
Interference: d1+d3≈0
Data: d2 survives
Interference: d1+d2≈0
Data: d3 survives
User 2 User 3 User 1
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User 1
Ethernet
AP1
User 2
AP2
User 3
AP3
User 10
AP10 …
…
Access Points act as a huge distributed MIMO transmitter with sum of antennas
10 Access Points 10x Higher Throughput
MegaMIMO = Distributed MIMO
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Testbed of Software Radios
Dense Conference Room Like Deployment
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0
50
100
150
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250
300
1 2 3 4 5 6 7 8 9 10
MegaMIMO
802.11
Total Throu
ghpu
t [Mb/s]
10x
10x throughput gain over existing Wi-Fi
Results from Prototype
Number of Access Points on Same Channel
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CenBmeter-‐Scale LocalizaBon
Today, RF-‐based localizaBon has about one meter accuracy
Challenge: MulBpath effects confuse the localizaBon system
SoluBon: Use the mulBpath reflecBon paVern as a signature of the locaBon
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0 50 100 1500
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Use MulPpath ReflecPons Pow
er of
Refle
cPon
SpaPal Angle (degree)
Receiver
35o
35o
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0 50 100 1500
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0 50 100 1500
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Pow
er of
Refle
cPon
SpaPal Angle (degree)
140o
140o
Use MulPpath ReflecPons
Receiver
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0 50 100 1500
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0 50 100 1500
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Pow
er of
Refle
cPon
SpaPal Angle (degree)
Use MulPpath ReflecPons
Receiver Can localize to within a few cenBmeters
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Works even with RFIDs
BaVery-‐free sBckers to tag any and every object
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No more customer checkout lines
RFIDs on goods
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No more customer checkout lines
RFIDs on Basket
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Red Pringles RFID
Shelf RFID
Basket RFID
Pringles
Basket
Shelf
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Red Pringles RFID
Basket RFID
Shelf RFID
Basket Shelf Pringles
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RFID-‐tagged Laptop
RFID-‐tagged Laptop Charger
RFID-‐tagged Handbag
Smart Homes
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Reference RFIDs
Smart Homes
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Smart Homes
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Charger le\ behind!
RFIDs on the Door Frame
Smart Homes
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Can your cellphone give you X-‐ray vision?
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WiVi: See through-‐walls with WiFi • WiFi signals traverse walls and reflect off objects
• Challenge: reflecBons off the separaBng wall are 10,000x higher than off a human behind the wall
• SoluBon: use two transmit antennas and one receive antenna; the two transmiVed waves cancel each other for staBc objects but not animated objects
• See video on YouTube hVps://www.youtube.com/watch?v=uJkQzLjYBFI
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Low-‐power RealBme GHz-‐Wide
Spectrum Sensing
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Imagine
A low-power cheap sensor that captures GHz-wide spectrum in realtime
Thousands of sensors to map spectrum usage Very efficient dynamic spectrum sharing Can detect fleeting signals like radar
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Realtime GHz Spectrum Sensing is Difficult • Today, sequential scanning of tens of MHz
Can easily miss radar signals
• Key Challenge: high-speed ADCs
Tens of MHz ADC < a dollar
Low-power High resolution
A Few GHz ADC Hundreds of dollars
10x more power Poor resolution
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Idea: Leverage Sparsity
Sparse recovery show that one can acquire sparse signals using sub-Nyquist sampling
SeaVle January 7, 2013 (Microso\ Spectrum observatory)
Sparse FFT No random sampling can use low-‐speed ADCs
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Benefits of Sparse FFT
• Sub-sample the data Can use low-speed ADCs • Very fast algorithm Lower-power consumption
• Used sparse FFT to build a GHz receiver from three 50 MHz ADCs
• Both senses and decodes sparse spectrum
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Realtime GHz Spectrum Sensing Cambridge, MA January 18 2013
Sense GHz using 3 tens of MHz ADCs
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Decoding Senders Randomly Hopping in a GHz
Number of MHz Senders Randomly Hopping in in 0.9 GHz
Both Senses and Decodes
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3D Photography Using Sparse FFT • Generate depth and perspecBve using a camera array • Images are correlated 4D frequencies are sparse • Goal: reduce the number of camera elements to enable implementaBon in a hand-‐held device
• SoluBon: Camera images are correlated Use sparse FFT
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Results show that we can accurately reconstruct unsampled camera outputs
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Conclusion
The future will be full of amazing wireless technologies that will
change our life